Simulation method and system for interlayer fracture behavior of carbon nanotubes based on multi-factor influence

Abstract

This invention discloses a simulation method and system for interlaminar fracture performance of carbon nanotubes based on multi-factor influences, belonging to the field of carbon nanotube simulation technology. It establishes a multi-factor environmental parameter field including ambient temperature, material moisture absorption rate, loading rate, and areal density. Next, microscopic images and process parameters are obtained through interlaminar fracture experiments to construct an experimental data sequence. Subsequently, a three-dimensional simulation model is established, and the initial dynamic performance is corrected using experimental images, while retaining the corresponding adjustment strategies. Based on this, a comparative model of the difference characteristics between environmental parameters and adjustment strategies is established, and a relationship model between the two is constructed. Finally, this relationship model is used to analyze subsequent environmental data to determine the adjustment strategy for the simulation model. This invention achieves deep coupling between experimental data and the simulation model, enabling automatic and accurate correction of simulation performance based on multi-factor environmental changes, significantly improving the realism and prediction accuracy of interlaminar fracture simulation of carbon nanotube composite materials.

Description

Technical Field

[0001] This invention relates to the field of carbon nanotube simulation technology, and in particular to a simulation method and system for interlayer fracture behavior of carbon nanotubes based on the influence of multiple factors. Background Technology

[0002] With the increasing demand for lightweight and high-strength materials in aerospace, rail transportation, and high-precision industries, carbon fiber reinforced polymer (CFRP) composites have been widely used. To further improve the interlaminar fracture toughness of composites, introducing carbon nanotubes (CNTs) as a reinforcing phase in the interlaminar layer has become a mainstream technique. However, the interlaminar fracture behavior of CNT-reinforced composites is highly complex and sensitive to multiple factors. In actual service environments, materials are not only subjected to mechanical loads but also to long-term temperature fluctuations and humid environments. The coupling between ambient temperature and material moisture absorption rate leads to matrix softening and interface performance degradation. Simultaneously, the rate of load application triggers the unique rate-sensitive bridging mechanism of CNTs. Existing interlaminar fracture simulation methods are mostly based on simplified cohesive force models, often neglecting the coupling effect of humid aging and dynamic load rates, and are difficult to accurately capture the nonlinear failure behaviors of CNTs at the microscopic level, such as bridging and pull-out. Furthermore, traditional simulation model parameters are mostly fixed values, lacking dynamic alignment and feedback correction mechanisms with microscopic morphological changes during the actual fracture process, resulting in significant deviations between the simulated crack propagation paths and energy release rates and the actual physical experimental results. Summary of the Invention

[0003] The purpose of this invention is to provide an optimized simulation method and system for interlayer fracture behavior of carbon nanotubes.

[0004] The present invention discloses a simulation method for interlaminar fracture behavior of carbon nanotubes based on the influence of multiple factors, comprising: Step S100: Establish a multi-factor environmental parameter field, including ambient temperature, material moisture absorption rate, load application rate, and areal density of the carbon nanotube film. Step S200: Perform interlaminar fracture experiments on multi-factor environmental parameter field data, and retain the experimental process data, including microscopic images of the fracture surface and several types of fracture process parameters. Based on the temporal correspondence, construct an experimental data sequence from the microscopic images and fracture process parameters. Step S300: Construct a three-dimensional interlaminar fracture performance simulation model, use multi-factor environmental parameter field data as input data to drive the three-dimensional interlaminar fracture performance simulation model to perform initial fracture dynamic performance, and adjust the initial fracture dynamic performance of the three-dimensional interlaminar fracture performance simulation model based on the microscopic images in the experimental data sequence to obtain the corresponding reference fracture dynamic performance, and retain the adjustment strategy. Step S400: Establish a first comparison model between multi-factor environmental parameter field data, establish a second comparison model between adjustment strategies, obtain the first difference feature and the second difference feature respectively, and establish a difference feature relationship model between the first difference feature and the second difference feature. Step S500: Analyze the multi-factor environmental parameter field data of the later stage using the differential characteristic relationship model, and determine the adjustment strategy for the dynamic performance of the initial fracture in the later stage.

[0005] In some embodiments disclosed in this invention, the method for constructing a three-dimensional interlaminar fracture simulation model includes: Step S301: Construct a three-dimensional solid model of several layers of carbon fiber composite material layup, and set a pre-fabricated crack region of preset thickness in the middle layer. Step S302: At the interlayer interface of the pre-crack leading edge, a carbon nanotube reinforcement region is defined by a thin-layer solid unit to simulate the distribution of a carbon nanotube film with a preset areal density. Step 303: Refine the mesh in the interlayer region where the crack propagates; Step S304: The carbon fiber layup adopts a three-dimensional orthogonal anisotropic constitutive model. By inputting the elastic modulus, shear modulus and Poisson's ratio at different temperatures and moisture absorption rates, the wet heat aging phenomenon of the matrix in the layer is simulated. Step S305: An improved multilinear cohesive constitutive model is used at the interlayer interface to describe the initial damage of the resin matrix and the unique bridging and hindering mechanism of carbon nanotubes. Step S306: Based on the custom material subroutine, the ambient temperature and moisture absorption rate are defined as state variables, and the initial strength and critical strain energy release rate of the cohesive unit are corrected in real time. Step S307: Introduce the displacement opening rate response model and use a logarithmic function or power law function to adjust the interface strength in real time to simulate the rate-sensitive characteristics under dynamic impact load. In step S308, the unit displacement is determined in the subroutine, so that the force response enters the carbon nanotube bridging stage after reaching the resin failure point, forming a trilinear or quadrlinear mechanical evolution path.

[0006] In some embodiments disclosed in this invention, the method for adjusting the initial dynamic fracture behavior of a three-dimensional interlaminar fracture simulation model includes: Step S309: Align and compare the crack propagation path and interface stress state in the initial fracture dynamic performance with the performance in the microscopic images captured by the ultra-high speed camera in the experimental sequence to identify the residual performance of the crack propagation path and interface stress state. Step S310: Analyze the residual performance and correct the interface strength parameters, stress distribution parameters, and critical energy release rate parameters in the multilinear cohesive constitutive model through the inversion algorithm until the microscopic failure characteristics such as carbon nanotube pull-out and stress distribution in the initial fracture dynamic performance match the performance of the microscopic image, thereby obtaining the reference fracture dynamic performance. Step S311: Record the parameter correction values ​​and correction weights performed to change the initial fracture dynamic performance to the reference fracture dynamic performance, and identify the parameter corrections and correction weights as the adjustment strategy.

[0007] In some embodiments disclosed in this invention, the method for determining the first difference feature and the second difference feature using a first comparison model and a second comparison model includes: Methods for determining the first differential feature include: Step S401: Construct a parameter change axis for each factor data in the multi-factor environmental parameter field data. Based on the relative relationship of the multi-factor environmental parameter field data, construct several relative first parameter change axes. Each first parameter change axis corresponds to a set of relative factor data. Based on the specific parameters of the relative factor data, find the corresponding first parameter mapping point on the first parameter change axis. Connect the corresponding first parameter mapping points to obtain the first parameter mapping line. The combination of each relative first parameter change axis and the first parameter mapping line is identified as the first factor data representation unit. Methods for determining the second differential feature include: Step S402: Construct a second parameter change axis for each factor data in the adjustment strategy. Based on the relative relationship of the adjustment strategy, construct several relative second parameter change axes. Based on the specific parameters of the relative factor data, find the corresponding second parameter mapping point on the second parameter change axis. Connect the corresponding second parameter mapping points to obtain the second parameter mapping line. The combination of each relative second parameter change axis and the second parameter mapping line is identified as the second factor data representation unit.

[0008] In some embodiments disclosed in this invention, the method for establishing a difference feature relationship model between a first difference feature and a second difference feature includes: Step S403: Classify all first factor data representation units among the first difference features according to their consistency to obtain several sets of first difference features, and perform representative analysis on the first difference features in each set of first difference features to determine several representative first difference features as index labels. Step S404: The first difference features marked as index labels are constructed into an index label comparison library to determine several corresponding first difference features, and then the second difference features corresponding to the first difference features are determined. The methods for representativeness analysis of the first difference feature in the first difference feature set include: Step S4031: Perform trend approximation comparison on the second difference features corresponding to each of the first difference features in the first difference feature set. This includes comparing each relative second factor data representation unit of the second difference feature. If the relative height and relative slope between the second parameter mapping lines of the second factor data representation units are both within a preset range, then the second factor data representation units are deemed to meet the trend approximation. If all the second factor data representation units in the second difference features meet the trend approximation, then the second difference features meet the trend approximation. Step S4032: The first difference features in the first difference feature set that meet the trend approximation are reclassified to obtain several subsets of the first difference features; Step S4033: The first difference features in the first difference feature subset are averaged, including calculating the average value of each first parameter mapping line to obtain the averaged first parameter mapping line, and taking all the averaged first parameter mapping lines as the representative first difference features.

[0009] In some embodiments disclosed in this invention, the method for analyzing subsequent multi-factor environmental parameter field data using a difference feature relationship model includes: Step S501: Compare the subsequent multi-factor environmental parameter field data with the historical multi-factor environmental parameter field data, determine the matching historical multi-factor environmental parameter field data, and determine the adjustment strategy for the subsequent initial fracture dynamic performance. Step S502: Analyze the first difference feature between the subsequent multi-factor environmental parameter field data and the historical multi-factor environmental parameter field data using the difference feature relationship model. Based on the first difference feature, determine the second difference feature. Based on the second difference feature, modify the adjustment strategy. Based on the modified adjustment strategy, adjust the initial fracture dynamic performance in the later stage to obtain the later reference fracture dynamic performance.

[0010] In some embodiments disclosed in this invention, the method for comparing subsequent multi-factor environmental parameter field data with historical multi-factor environmental parameter field data includes: Step S5011: Compare and analyze each relative factor data among the multi-factor environmental parameter field data, calculate the parameter difference of each relative factor data, determine the preset parameter segment to which the parameter of the factor data belongs in the preset weight comparison table, and then determine the weight coefficient corresponding to the preset parameter segment. Step S5012: Based on the preset difference range to which the parameter difference belongs, determine the corresponding sub-parameter of the parameter difference, and combine it with the weight coefficient to determine the matching parameter between the factor data. Step S5013: Based on the consistency parameters among all factor data, determine the degree of consistency among multi-factor environmental parameter field data; The expression for calculating the degree of agreement is: ; Where F represents the degree of consistency. Let n be the consistency parameter for the i-th factor data, and n be the number of factors in the data. To adjust the coefficients for the combined effects of multiple factors, we analyze the preset parameter intervals to which the data for each factor belong, and based on these preset parameter intervals, we determine the... The output value.

[0011] In some embodiments disclosed in this invention, a simulation system for interlaminar fracture behavior of carbon nanotubes based on the influence of multiple factors is also disclosed, including: The first module is used to establish a multi-factor environmental parameter field, including ambient temperature, material moisture absorption rate, load application rate, and areal density of carbon nanotube films. The second module is used to conduct interlaminar fracture experiments on multi-factor environmental parameter field data and retain the experimental process data, including microscopic images of the fracture surface and several types of fracture process parameters. Based on the temporal correspondence, the microscopic images and fracture process parameters are constructed into an experimental data sequence. The third module is used to construct a three-dimensional interlaminar fracture performance simulation model. It takes multi-factor environmental parameter field data as input data, drives the three-dimensional interlaminar fracture performance simulation model to perform initial fracture dynamic performance, and adjusts the initial fracture dynamic performance of the three-dimensional interlaminar fracture performance simulation model based on microscopic images in the experimental data sequence to obtain the corresponding reference fracture dynamic performance, and retains the adjustment strategy. The fourth module is used to establish a first comparison model between multi-factor environmental parameter field data, establish a second comparison model between adjustment strategies, obtain the first difference feature and the second difference feature respectively, and establish a difference feature relationship model between the first difference feature and the second difference feature. The fifth module is used to analyze the multi-factor environmental parameter field data in the later stage using the differential characteristic relationship model, and to determine the adjustment strategy for the dynamic performance of the initial fracture in the later stage.

[0012] This invention discloses a simulation method and system for interlaminar fracture performance of carbon nanotubes based on multi-factor influences, belonging to the field of carbon nanotube simulation technology. It establishes a multi-factor environmental parameter field including ambient temperature, material moisture absorption rate, loading rate, and areal density. Next, microscopic images and process parameters are obtained through interlaminar fracture experiments to construct an experimental data sequence. Subsequently, a three-dimensional simulation model is established, and the initial dynamic performance is corrected using experimental images, while retaining the corresponding adjustment strategies. Based on this, a comparative model of the difference characteristics between environmental parameters and adjustment strategies is established, and a relationship model between the two is constructed. Finally, this relationship model is used to analyze subsequent environmental data to determine the adjustment strategy for the simulation model. This invention achieves deep coupling between experimental data and the simulation model, enabling automatic and accurate correction of simulation performance based on multi-factor environmental changes, significantly improving the realism and prediction accuracy of interlaminar fracture simulation of carbon nanotube composite materials.

[0013] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating the method steps of the simulation method for interlayer fracture behavior of carbon nanotubes based on the influence of multiple factors disclosed in this embodiment of the invention. Detailed Implementation

[0015] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0016] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments. It should be understood that the preferred embodiments described herein are only for illustration and explanation of the present invention and should not be construed as limiting the scope of protection of the present invention. Those skilled in the art can make some non-essential improvements and adjustments based on the following content of the present invention. In the present invention, unless otherwise expressly specified and limited, the technical terms used in the present invention should have the ordinary meaning understood by those skilled in the art.

[0017] Example: The present invention discloses a simulation method for interlaminar fracture behavior of carbon nanotubes based on the influence of multiple factors. (See attached document.) Figure 1 ,include: Step S100: Establish a multi-factor environmental parameter field, including ambient temperature, material moisture absorption rate, load application rate, and areal density of the carbon nanotube film.

[0018] The core of this step lies in transforming the complex variables affecting material lifespan in the real world into measurable physical fields. Instead of solely considering mechanical loads, it integrates external environmental heat distribution, the degree of moisture infiltration within the material, the dynamic rate of the pressure application process, and the distribution density of the reinforcing phase material. The underlying logic is to establish a multi-dimensional reference framework for subsequent research, ensuring that all observations can be correlated within a specific physical context. This is akin to meticulously replicating the terrain, climate, and supply conditions before conducting a precise exercise, providing a solid foundation of underlying data for subsequent failure analysis.

[0019] Step S200: Perform interlaminar fracture experiments on multi-factor environmental parameter field data, and retain the experimental process data, including microscopic images of the fracture surface and several types of fracture process parameters. Based on the temporal correspondence, construct an experimental data sequence from the microscopic images and fracture process parameters.

[0020] This step obtains real evidence of the material failure process through spatiotemporal synchronization. The principle lies in using high-precision observation equipment to capture the microscopic appearance of crack propagation at the instant and aligning it precisely with the mechanical feedback captured by sensors on the timeline. This approach overcomes the shortcomings of past methods that only considered experimental results while ignoring the evolutionary process, transforming visual morphological evolution into a logical sequence that can explain mechanical fluctuations. For example, when we observe a large area of ​​reinforcing fibers being pulled out from within the material at a given time point, the data sequence simultaneously records the dramatic fluctuations in load-bearing capacity at that moment, thus establishing a strong causal relationship between physical phenomena and numerical changes.

[0021] Step S300: Construct a three-dimensional interlaminar fracture performance simulation model, using multi-factor environmental parameter field data as input data to drive the three-dimensional interlaminar fracture performance simulation model to perform initial fracture dynamic performance, and adjust the initial fracture dynamic performance of the three-dimensional interlaminar fracture performance simulation model based on microscopic images in the experimental data sequence to obtain the corresponding reference fracture dynamic performance, and retain the adjustment strategy.

[0022] This stage employs a model optimization algorithm based on real-world feedback. Initial mathematical models are often based on idealized assumptions and struggle to fully replicate the complex mechanical contributions of microscopic interfaces. By inputting the aforementioned environmental parameters into a digital system, an initial simulated performance is generated and compared with real microscopic evolution images. If the simulated crack path deviates from reality, key parameters within the model are corrected through reverse engineering until the digital image can highly reproduce the details of damage in reality. The parameter correction methods generated in this process are fully preserved, forming a library of correction strategies with practical value.

[0023] Step S400: Establish a first comparison model between multi-factor environmental parameter field data, establish a second comparison model between adjustment strategies, obtain the first difference feature and the second difference feature respectively, and establish a difference feature relationship model between the first difference feature and the second difference feature.

[0024] This step aims to extract deeper physical laws from data discrepancies. By comparing parameter fluctuations under different environmental conditions and the changes in correction strategies adopted to match these fluctuations, a feature mapping system is established between the two. The principle is to explore the functional relationship between changes in environmental factors and model bias. Simply put, it involves analyzing past experience in handling different situations to summarize the general rules of how the simulation logic should adjust when the environment changes. This is equivalent to installing an intelligent brain in the simulation system, giving it the logical perception ability to deduce unknown conditions from known conditions.

[0025] Step S500: Analyze the multi-factor environmental parameter field data of the later stage using the differential characteristic relationship model, and determine the adjustment strategy for the dynamic performance of the initial fracture in the later stage.

[0026] The final stage achieved a leap in simulation technology from passive fitting to active prediction. Utilizing a pre-built feature mapping system, when faced with a completely new environment that has not been experimentally verified, the system no longer relies on repetitive physical tests. It automatically identifies the characteristic differences between the new environment and existing experience, and uses the mastered correlation rules to directly calculate the optimal correction scheme for the new scenario. This process significantly shortens the performance evaluation cycle of new materials under extreme or complex conditions, ensuring that even in the absence of experimental support, highly accurate simulation results can be obtained through logical extrapolation.

[0027] In some embodiments disclosed in this invention, the method for constructing a three-dimensional interlaminar fracture simulation model includes: Step S301: Construct a three-dimensional solid model of several layers of carbon fiber composite material layup, and set a pre-fabricated crack region of preset thickness in the middle layer.

[0028] Step S302: At the interlayer interface of the pre-crack leading edge, a carbon nanotube reinforcement region is defined by a thin-layer solid unit to simulate the distribution of a carbon nanotube film with a preset areal density.

[0029] Step 303: Perform mesh refinement on the interlayer region where crack propagation occurs.

[0030] In step S304, the carbon fiber layup adopts a three-dimensional orthogonal anisotropic constitutive model. By inputting the elastic modulus, shear modulus and Poisson's ratio at different temperatures and moisture absorption rates, the wet heat aging phenomenon of the matrix in the layer is simulated.

[0031] In step S305, an improved multilinear cohesive constitutive model is used at the interlayer interface to describe the initial damage to the resin matrix and the unique bridging and hindering mechanism of carbon nanotubes.

[0032] Step S306: Based on the custom material subroutine, the ambient temperature and moisture absorption rate are defined as state variables, and the initial strength and critical strain energy release rate of the cohesive unit are corrected in real time.

[0033] Step S307 introduces a displacement opening rate response model and uses a logarithmic function or power law function to adjust the interface strength in real time to simulate the rate-sensitive characteristics under dynamic impact load.

[0034] In step S308, the unit displacement is determined in the subroutine, so that the force response enters the carbon nanotube bridging stage after reaching the resin failure point, forming a trilinear or quadrlinear mechanical evolution path.

[0035] In some embodiments disclosed in this invention, the method for adjusting the initial dynamic fracture behavior of a three-dimensional interlaminar fracture simulation model includes: Step S309: Align and compare the crack propagation path and interface stress state in the initial fracture dynamic performance with the performance in the microscopic images captured by the ultra-high speed camera in the experimental sequence to identify the residual performance of the crack propagation path and interface stress state.

[0036] Step S310: Analyze the residual performance and correct the interface strength parameters, stress distribution parameters, and critical energy release rate parameters in the multilinear cohesive constitutive model through the inversion algorithm until the microscopic failure characteristics such as carbon nanotube pull-out and stress distribution in the initial fracture dynamic performance match the performance of the microscopic image, thereby obtaining the reference fracture dynamic performance.

[0037] Step S311: Record the parameter correction values ​​and correction weights performed to change the initial fracture dynamic performance to the reference fracture dynamic performance, and identify the parameter corrections and correction weights as the adjustment strategy.

[0038] In some embodiments disclosed in this invention, the method for determining the first difference feature and the second difference feature using a first comparison model and a second comparison model includes: Methods for determining the first differential feature include: Step S401: Construct a parameter change axis for each factor data in the multi-factor environmental parameter field data. Based on the relative relationship of the multi-factor environmental parameter field data, construct several relative first parameter change axes. Each first parameter change axis corresponds to a set of relative factor data. Based on the specific parameters of the relative factor data, find the corresponding first parameter mapping point on the first parameter change axis. Connect the corresponding first parameter mapping points to obtain the first parameter mapping line. The combination of each relative first parameter change axis and the first parameter mapping line is identified as the first factor data representation unit.

[0039] Methods for determining the second differential feature include: Step S402: Construct a second parameter change axis for each factor data in the adjustment strategy. Based on the relative relationship of the adjustment strategy, construct several relative second parameter change axes. Based on the specific parameters of the relative factor data, find the corresponding second parameter mapping point on the second parameter change axis. Connect the corresponding second parameter mapping points to obtain the second parameter mapping line. The combination of each relative second parameter change axis and the second parameter mapping line is identified as the second factor data representation unit.

[0040] The second method involves the characteristic recombination of simulation correction logic, transforming it from numerical adjustment to morphological representation. By constructing dedicated evaluation axes for each correction parameter in the adjustment strategy and projecting specific weights and correction values ​​onto corresponding mapping points, the overall structure of the optimization strategy is outlined using trajectory lines. This process essentially condenses the abstract model correction behavior into geometric units with spatial characteristics, making the complex parameter optimization logic intuitively comparable and quantifiable. This approach not only reveals the distribution patterns of the optimization strategy across different dimensions but also provides morphological evidence for ultimately revealing the intrinsic relationship between environmental fluctuations and model feedback.

[0041] In some embodiments disclosed in this invention, the method for establishing a difference feature relationship model between a first difference feature and a second difference feature includes: Step S403: Classify all first factor data representation units among the first difference features according to their consistency to obtain several sets of first difference features, and perform representative analysis on the first difference features in each set of first difference features to determine several representative first difference features as index labels.

[0042] Step S404: The first difference features marked as index labels are constructed into an index label comparison library to determine several corresponding first difference features, and then the second difference features corresponding to the first difference features are determined.

[0043] The methods for representativeness analysis of the first difference feature in the first difference feature set include: Step S4031: Perform trend approximation comparison on the second difference features corresponding to each of the first difference features in the first difference feature set. This includes comparing each relative second factor data representation unit of the second difference feature. If the relative height and relative slope between the second parameter mapping lines of the second factor data representation units are both within a preset range, then the second factor data representation units are deemed to meet the trend approximation. If all the second factor data representation units in the second difference features meet the trend approximation, then the second difference features meet the trend approximation.

[0044] Step S4032: The first difference features in the first difference feature set that meet the trend approximation are reclassified to obtain several subsets of the first difference features.

[0045] Step S4033: The first difference features in the first difference feature subset are averaged, including calculating the average value of each first parameter mapping line to obtain the averaged first parameter mapping line, and taking all the averaged first parameter mapping lines as the representative first difference features.

[0046] The core logic of this step lies in clustering and dimensionality reduction, as well as extracting commonalities from environmental differences. The principle is to categorize massive amounts of environmental fluctuation units according to their degree of similarity, thereby filtering out representative benchmarks from the complex raw data. Through this classification method, the system can aggregate scattered environmental features into several feature sets and extract core identifiers that represent the type of working condition as index labels. This is akin to creating a classification catalog within a vast number of cases, not only simplifying the data structure but also providing clear logical classification support for the simulation system when facing complex variables by identifying representative features.

[0047] The underlying principle of representativeness analysis is a refined screening based on response consistency. Its logic lies in the assumption that environmental features are only classified as similar when different environmental fluctuations trigger correction strategies with highly similar evolutionary trends. In practice, the system rigorously compares the relative positions and deflection angles of the correction curves in geometric space to ensure that their fluctuation amplitude and rate of change are within a very small deviation range. Once a set of correction strategies is confirmed to perfectly match in dynamic trends, the corresponding environmental features are further refined, and averaging is used to merge multiple geometric lines into a standardized ideal trajectory. This approach eliminates the random errors that may exist with single data points, resulting in index labels with extremely high statistical stability and physical representativeness.

[0048] The index label comparison library is built upon the principle of establishing a deterministic mapping mechanism from the physical environment to the simulation logic. By using synthesized representative features as the core of retrieval, a highly efficient knowledge matching engine is established, essentially achieving a logical closed loop from cause to countermeasure. When new environmental differences emerge, the comparison library can quickly identify the closest benchmark label and accurately deduce the associated model correction scheme. This process transforms fragmented experimental experience into a structured expert knowledge base, enabling the simulation system to quickly optimize and adaptively align the simulation trajectory based on existing successful correction examples when processing later data.

[0049] In some embodiments disclosed in this invention, the method for analyzing subsequent multi-factor environmental parameter field data using a difference feature relationship model includes: Step S501: Compare the subsequent multi-factor environmental parameter field data with the historical multi-factor environmental parameter field data, determine the matching historical multi-factor environmental parameter field data, and determine the adjustment strategy for the subsequent initial fracture dynamic performance. Step S502: Analyze the first difference feature between the subsequent multi-factor environmental parameter field data and the historical multi-factor environmental parameter field data using the difference feature relationship model. Based on the first difference feature, determine the second difference feature. Based on the second difference feature, modify the adjustment strategy. Based on the modified adjustment strategy, adjust the initial fracture dynamic performance in the later stage to obtain the later reference fracture dynamic performance.

[0050] In some embodiments disclosed in this invention, the method for comparing subsequent multi-factor environmental parameter field data with historical multi-factor environmental parameter field data includes: Step S5011: Compare and analyze each relative factor data among the multi-factor environmental parameter field data, calculate the parameter difference of each relative factor data, determine the preset parameter segment to which the parameter of the factor data belongs in the preset weight comparison table, and then determine the weight coefficient corresponding to the preset parameter segment. Step S5012: Based on the preset difference range to which the parameter difference belongs, determine the corresponding sub-parameter of the parameter difference, and combine it with the weight coefficient to determine the matching parameter between the factor data. Step S5013: Based on the consistency parameters among all factor data, determine the degree of consistency among multi-factor environmental parameter field data; The expression for calculating the degree of agreement is: ; Where F represents the degree of consistency. Let n be the consistency parameter for the i-th factor data, and n be the number of factors in the data. To adjust the coefficients for the combined effects of multiple factors, we analyze the preset parameter intervals to which the data for each factor belong, and based on these preset parameter intervals, we determine the... The output value.

[0051] Multi-factor combined effect adjustment coefficient: This is a non-linear correction coefficient. Its function is to take into account the "superposition effect" or "coupling effect" produced when multiple factors change simultaneously.

[0052] The system will analyze each The corresponding interval is determined based on the distribution of these individual indicators. The value of . For example, if all factors are very close, This might reinforce the similarity; if a core factor differs significantly, This will have an inhibitory effect.

[0053] In some embodiments disclosed in this invention, a simulation system for interlaminar fracture behavior of carbon nanotubes based on the influence of multiple factors is also disclosed, including: The first module is used to establish a multi-factor environmental parameter field, including ambient temperature, material moisture absorption rate, load application rate, and areal density of carbon nanotube films. The second module is used to conduct interlaminar fracture experiments on multi-factor environmental parameter field data and retain the experimental process data, including microscopic images of the fracture surface and several types of fracture process parameters. Based on the temporal correspondence, the microscopic images and fracture process parameters are constructed into an experimental data sequence. The third module is used to construct a three-dimensional interlaminar fracture performance simulation model. It takes multi-factor environmental parameter field data as input data, drives the three-dimensional interlaminar fracture performance simulation model to perform initial fracture dynamic performance, and adjusts the initial fracture dynamic performance of the three-dimensional interlaminar fracture performance simulation model based on microscopic images in the experimental data sequence to obtain the corresponding reference fracture dynamic performance, and retains the adjustment strategy. The fourth module is used to establish a first comparison model between multi-factor environmental parameter field data, establish a second comparison model between adjustment strategies, obtain the first difference feature and the second difference feature respectively, and establish a difference feature relationship model between the first difference feature and the second difference feature. The fifth module is used to analyze the multi-factor environmental parameter field data in the later stage using the differential characteristic relationship model, and to determine the adjustment strategy for the dynamic performance of the initial fracture in the later stage.

[0054] This invention discloses a simulation method and system for interlaminar fracture performance of carbon nanotubes based on multi-factor influences, belonging to the field of carbon nanotube simulation technology. It establishes a multi-factor environmental parameter field including ambient temperature, material moisture absorption rate, loading rate, and areal density. Next, microscopic images and process parameters are obtained through interlaminar fracture experiments to construct an experimental data sequence. Subsequently, a three-dimensional simulation model is established, and the initial dynamic performance is corrected using experimental images, while retaining the corresponding adjustment strategies. Based on this, a comparative model of the difference characteristics between environmental parameters and adjustment strategies is established, and a relationship model between the two is constructed. Finally, this relationship model is used to analyze subsequent environmental data to determine the adjustment strategy for the simulation model. This invention achieves deep coupling between experimental data and the simulation model, enabling automatic and accurate correction of simulation performance based on multi-factor environmental changes, significantly improving the realism and prediction accuracy of interlaminar fracture simulation of carbon nanotube composite materials.

[0055] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented in hardware or by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A simulation method for interlaminar fracture behavior of carbon nanotubes based on the influence of multiple factors, characterized in that, include: Step S100: Establish a multi-factor environmental parameter field, including ambient temperature, material moisture absorption rate, load application rate, and areal density of the carbon nanotube film. Step S200: Perform interlaminar fracture experiments on multi-factor environmental parameter field data, and retain the experimental process data, including microscopic images of the fracture surface and several types of fracture process parameters. Based on the temporal correspondence, construct an experimental data sequence from the microscopic images and fracture process parameters. Step S300: Construct a three-dimensional interlaminar fracture performance simulation model, use multi-factor environmental parameter field data as input data to drive the three-dimensional interlaminar fracture performance simulation model to perform initial fracture dynamic performance, and adjust the initial fracture dynamic performance of the three-dimensional interlaminar fracture performance simulation model based on the microscopic images in the experimental data sequence to obtain the corresponding reference fracture dynamic performance, and retain the adjustment strategy. Step S400: Establish a first comparison model between multi-factor environmental parameter field data, establish a second comparison model between adjustment strategies, obtain the first difference feature and the second difference feature respectively, and establish a difference feature relationship model between the first difference feature and the second difference feature. Step S500: Analyze the multi-factor environmental parameter field data of the later stage using the differential characteristic relationship model, and determine the adjustment strategy for the dynamic performance of the initial fracture in the later stage.

2. The simulation method for interlaminar fracture behavior of carbon nanotubes based on the influence of multiple factors according to claim 1, characterized in that, Methods for constructing three-dimensional interlaminar fracture simulation models include: Step S301: Construct a three-dimensional solid model of several layers of carbon fiber composite material layup, and set a pre-fabricated crack region of preset thickness in the middle layer. Step S302: At the interlayer interface of the pre-crack leading edge, a carbon nanotube reinforcement region is defined by a thin-layer solid unit to simulate the distribution of a carbon nanotube film with a preset areal density. Step 303: Refine the mesh in the interlayer region where the crack propagates; Step S304: The carbon fiber layup adopts a three-dimensional orthogonal anisotropic constitutive model. By inputting the elastic modulus, shear modulus and Poisson's ratio at different temperatures and moisture absorption rates, the wet heat aging phenomenon of the matrix in the layer is simulated. Step S305: An improved multilinear cohesive constitutive model is used at the interlayer interface to describe the initial damage of the resin matrix and the unique bridging and hindering mechanism of carbon nanotubes. Step S306: Based on the custom material subroutine, the ambient temperature and moisture absorption rate are defined as state variables, and the initial strength and critical strain energy release rate of the cohesive unit are corrected in real time. Step S307: Introduce the displacement opening rate response model and use a logarithmic function or power law function to adjust the interface strength in real time to simulate the rate-sensitive characteristics under dynamic impact load. In step S308, the unit displacement is determined in the subroutine, so that the force response enters the carbon nanotube bridging stage after reaching the resin failure point, forming a trilinear or quadrlinear mechanical evolution path.

3. The simulation method for interlaminar fracture behavior of carbon nanotubes based on the influence of multiple factors according to claim 1, characterized in that, Methods for adjusting the initial fracture dynamics of a three-dimensional interlaminar fracture simulation model include: Step S309: Align and compare the crack propagation path and interface stress state in the initial fracture dynamic performance with the performance in the microscopic images captured by the ultra-high speed camera in the experimental sequence to identify the residual performance of the crack propagation path and interface stress state. Step S310: Analyze the residual performance and correct the interface strength parameters, stress distribution parameters, and critical energy release rate parameters in the multilinear cohesive constitutive model through the inversion algorithm until the microscopic failure characteristics such as carbon nanotube pull-out and stress distribution in the initial fracture dynamic performance match the performance of the microscopic image, thereby obtaining the reference fracture dynamic performance. Step S311: Record the parameter correction values ​​and correction weights performed to change the initial fracture dynamic performance to the reference fracture dynamic performance, and identify the parameter corrections and correction weights as the adjustment strategy.

4. The simulation method for interlaminar fracture behavior of carbon nanotubes based on the influence of multiple factors according to claim 3, characterized in that, Methods for determining the first and second difference features using the first and second comparison models include: Methods for determining the first differential feature include: Step S401: Construct a parameter change axis for each factor data in the multi-factor environmental parameter field data. Based on the relative relationship of the multi-factor environmental parameter field data, construct several relative first parameter change axes. Each first parameter change axis corresponds to a set of relative factor data. Based on the specific parameters of the relative factor data, find the corresponding first parameter mapping point on the first parameter change axis. Connect the corresponding first parameter mapping points to obtain the first parameter mapping line. The combination of each relative first parameter change axis and the first parameter mapping line is identified as the first factor data representation unit. Methods for determining the second differential feature include: Step S402: Construct a second parameter change axis for each factor data in the adjustment strategy. Based on the relative relationship of the adjustment strategy, construct several relative second parameter change axes. Based on the specific parameters of the relative factor data, find the corresponding second parameter mapping point on the second parameter change axis. Connect the corresponding second parameter mapping points to obtain the second parameter mapping line. The combination of each relative second parameter change axis and the second parameter mapping line is identified as the second factor data representation unit.

5. The simulation method for interlaminar fracture behavior of carbon nanotubes based on the influence of multiple factors according to claim 4, characterized in that, Methods for establishing a differential feature relationship model between the first and second differential features include: Step S403: Classify all first factor data representation units among the first difference features according to their consistency to obtain several sets of first difference features, and perform representative analysis on the first difference features in each set of first difference features to determine several representative first difference features as index labels. Step S404: The first difference features marked as index labels are constructed into an index label comparison library to determine several corresponding first difference features, and then the second difference features corresponding to the first difference features are determined. The methods for representativeness analysis of the first difference feature in the first difference feature set include: Step S4031: Perform trend approximation comparison on the second difference features corresponding to each of the first difference features in the first difference feature set. This includes comparing each relative second factor data representation unit of the second difference feature. If the relative height and relative slope between the second parameter mapping lines of the second factor data representation units are both within a preset range, then the second factor data representation units are deemed to meet the trend approximation. If all the second factor data representation units in the second difference features meet the trend approximation, then the second difference features meet the trend approximation. Step S4032: The first difference features in the first difference feature set that meet the trend approximation are reclassified to obtain several subsets of the first difference features; Step S4033: The first difference features in the first difference feature subset are averaged, including calculating the average value of each first parameter mapping line to obtain the averaged first parameter mapping line, and taking all the averaged first parameter mapping lines as the representative first difference features.

6. The simulation method for interlaminar fracture behavior of carbon nanotubes based on the influence of multiple factors according to claim 4, characterized in that, Methods for analyzing multi-factor environmental parameter field data using differential characteristic relationship models include: Step S501: Compare the subsequent multi-factor environmental parameter field data with the historical multi-factor environmental parameter field data, determine the matching historical multi-factor environmental parameter field data, and determine the adjustment strategy for the subsequent initial fracture dynamic performance. Step S502: Analyze the first difference feature between the subsequent multi-factor environmental parameter field data and the historical multi-factor environmental parameter field data using the difference feature relationship model. Based on the first difference feature, determine the second difference feature. Based on the second difference feature, modify the adjustment strategy. Based on the modified adjustment strategy, adjust the initial fracture dynamic performance in the later stage to obtain the later reference fracture dynamic performance.

7. The simulation method for interlaminar fracture behavior of carbon nanotubes based on the influence of multiple factors according to claim 6, characterized in that, Methods for comparing later-stage multi-factor environmental parameter field data with historical multi-factor environmental parameter field data include: Step S5011: Compare and analyze each relative factor data among the multi-factor environmental parameter field data, calculate the parameter difference of each relative factor data, determine the preset parameter segment to which the parameter of the factor data belongs in the preset weight comparison table, and then determine the weight coefficient corresponding to the preset parameter segment. Step S5012: Based on the preset difference range to which the parameter difference belongs, determine the corresponding sub-parameter of the parameter difference, and combine it with the weight coefficient to determine the matching parameter between the factor data. Step S5013: Based on the consistency parameters among all factor data, determine the degree of consistency among multi-factor environmental parameter field data; The expression for calculating the degree of agreement is: ； Where F represents the degree of consistency. Let n be the consistency parameter for the i-th factor data, and n be the number of factors in the data. To adjust the coefficients for the combined effects of multiple factors, we analyze the preset parameter intervals to which the data for each factor belong, and based on these preset parameter intervals, we determine the... The output value.

8. A simulation system for interlaminar fracture behavior of carbon nanotubes based on the influence of multiple factors, characterized in that, include: The first module is used to establish a multi-factor environmental parameter field, including ambient temperature, material moisture absorption rate, load application rate, and areal density of carbon nanotube films. The second module is used to conduct interlaminar fracture experiments on multi-factor environmental parameter field data and retain the experimental process data, including microscopic images of the fracture surface and several types of fracture process parameters. Based on the temporal correspondence, the microscopic images and fracture process parameters are constructed into an experimental data sequence. The third module is used to construct a three-dimensional interlaminar fracture performance simulation model. It takes multi-factor environmental parameter field data as input data, drives the three-dimensional interlaminar fracture performance simulation model to perform initial fracture dynamic performance, and adjusts the initial fracture dynamic performance of the three-dimensional interlaminar fracture performance simulation model based on microscopic images in the experimental data sequence to obtain the corresponding reference fracture dynamic performance, and retains the adjustment strategy. The fourth module is used to establish a first comparison model between multi-factor environmental parameter field data, establish a second comparison model between adjustment strategies, obtain the first difference feature and the second difference feature respectively, and establish a difference feature relationship model between the first difference feature and the second difference feature. The fifth module is used to analyze the multi-factor environmental parameter field data in the later stage using the differential characteristic relationship model, and to determine the adjustment strategy for the dynamic performance of the initial fracture in the later stage.

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